Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims 1, 29, and 31 are provisionally rejected on the ground of non-statutory obviousness-type double patenting as being unpatentable over claims 7, 17, and 27 of co-pending U.S. Serial No. 18/689,053. It would have been obvious to one of ordinary skill in the art to omit the step of using an excitation signal or non-linear operations, thus amounting to a broader representation, In re Karlson 136 USPQ 184 (1963): "Omission of an element and its function is an obvious expedient if the remaining elements perform the same functions as before"
This is a provisional obviousness-type double patenting rejection because the conflicting claims have not in fact been patented.
Present invention 18/689,053
1. (Original) A device comprising: a neural network configured to process one or more neural network inputs to generate a joint probability distribution, the one or more neural network inputs including at least first previous sample data and second previous sample data associated with at least one previous data sample of a sequence of data samples; and a sample generator configured to generate first sample data and second sample data based on the joint probability distribution, the first sample data and the second sample data associated with at least one data sample of the sequence of data samples.
7. (Original) The apparatus of claim 6, wherein, to generate the excitation signal using the first neural network, the at least one processor is configured to: generate, using the one or more inputs to the first neural network, a probability distribution by providing the one or more inputs to the non-linear likelihood speech model; determine one or more samples from the generated probability distribution; and generate, using the one or more samples from the generated probability distribution, the excitation signal.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3, 4, 7-19, 24, and 27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception such as a natural phenomenon, abstract idea, or law of nature, without significantly more and/or a practical application per se, specifically with one or more of:
1) Not integrating a judicial exception into a practical application (see explanation below), and
2) Not reciting elements that would amount to significantly more than the judicial exception (see explanation below).
Accordingly, claims 1, 3, 4, 7-19, 24, and 27 are directed towards patent ineligible subject matter under 35 U.S.C. 101.
The independent claims:
When taking the current claim limitations of the present invention, we see that they are directed to using joint probability distribution from a previous sample set to produce new samples such as through LPC mathematical operations.
Regarding the claim limitations of claim(s) 1, 29, and 31 as recited:
1. (Original) A device comprising: a neural network configured to process one or more neural network inputs to generate a joint probability distribution, the one or more neural network inputs including at least first previous sample data and second previous sample data associated with at least one previous data sample of a sequence of data samples; and a sample generator configured to generate first sample data and second sample data based on the joint probability distribution, the first sample data and the second sample data associated with at least one data sample of the sequence of data samples.
Step 1: IS THE CLAIM DIRECTED TO A PROCESS, MACHINE, MANUFACTURE OR COMPOSITION OF MATTER?
Yes
Step 2A.1: IS THE CLAIM DIRECTED TO A LAW OF NATURE, A NATURAL PHENOMENON (PRODUCT OF NATURE) OR AN ABSTRACT IDEA?
Yes
Step 2A.2: DOES THE CLAIM RECITE ADDITIONAL ELEMENTS THAT INTEGRATE THE JUDICIAL EXCEPTION INTO A PRACTICAL APPLICATION?
No. Regarding the independent claims. No, analogous to Solutran, Inc. v. Elavon, Inc., 931 F.3d 1161, 2019 USPQ2d 281076 (Fed. Cir. 2019), the claims are directed to using joint probability distribution from a previous sample set to produce new samples such as through LPC mathematical operations, such as lacking a clear improvement of function/technology.
Further as demonstrated in Solutran, Inc. v. Elavon, Inc., 931 F.3d 1161, 2019 USPQ2d 281076 (Fed. Cir. 2019), the claims were to methods for electronically processing paper checks, all of which contained limitations setting forth receiving merchant transaction data from a merchant, crediting a merchant’s account, and receiving and scanning paper checks after the merchant’s account is credited. In part one of the Alice/Mayo test, the Federal Circuit determined that the claims were directed to the abstract idea of crediting the merchant’s account before the paper check is scanned. The court first determined that the recited limitations of “crediting a merchant’s account as early as possible while electronically processing a check” is a “long-standing commercial practice” like in Alice and Bilski. 931 F.3d at 1167, 2019 USPQ2d 281076, at *5 (Fed. Cir. 2019). The Federal Circuit then continued with its analysis under part one of the Alice/Mayo test finding that the claims are not directed to an improvement in the functioning of a computer or an improvement to another technology. In particular, the court determined that the claims “did not improve the technical capture of information from a check to create a digital file or the technical step of electronically crediting a bank account” nor did the claims “improve how a check is scanned.” Id.
Regarding the December 5th 2025 Memo in light of September 26, 2025 Appeals Review Panel Decision in Ex parte Desjardins, Appeal 2024-000567 for Application 16/319,040, in deciding if a recited abstract idea does or does not direct the entire claim to an abstract idea, when a claim is considered as a whole.
The claim which demonstrated improvements to technology and/or function recites: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task.".
The decision recites that “We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation.”
When considering the limitation decided upon, there are clear improvements to machine learning that are not rudimentary or a long-standing practice, for instance adjusting for optimization and protection of performance, as claimed, are improvements to a machine learning models operations, not simply a general mathematical or generic recitation, but rather an improvement to function.
Specifically, Ex Parte Desjardins explained the following:
Enfish ranks among the Federal Circuit's leading cases on the eligibility of technological improvements. In particular, Enfish recognized that “[m]uch of the advancement made in computer technology consists of improvements to software that, by their very nature, may not be defined by particular physical features but rather by logical structures and processes.” 822 F.3d at 1339. Moreover, because “[s]oftware can make non-abstract improvements to computer technology, just as hardware improvements can,” the Federal Circuit held that the eligibility determinations should turn on whether “the claims are directed to an improvement to computer functionality versus being directed to an abstract idea.” Id. at 1336. (Desjardins, page 8).
Further, specifically:
“Paragraph 21 of the Specification, which the Appellant cites, identifies improvements in training the machine learning model itself. Of course, such an assertion in the Specification alone is insufficient to support a patent eligibility determination, absent a subsequent determination that the claim itself reflects the disclosed improvement. See MPEP § 2106.05(a) (citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316 (Fed. Cir. 2016)). Here, however, we are persuaded that the claims reflect such an improvement. For example, one improvement identified in the 8 Appeal2024-000567 Application 16/319,040 Specification is to "effectively learn new tasks in succession whilst protecting knowledge about previous tasks." Spec. ,r 21. The Specification also recites that the claimed improvement allows artificial intelligence (AI) systems to "us[e] less of their storage capacity" and enables "reduced system complexity." Id. When evaluating the claim as a whole, we discern at least the following limitation of independent claim 1 that reflects the improvement: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task." We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation. Under a charitable view, the overbroad reasoning of the original panel below is perhaps understandable given the confusing nature of existing § 101 jurisprudence, but troubling, because this case highlights what is at stake. Categorically excluding AI innovations from patent protection in the United States jeopardizes America's leadership in this critical emerging technology. Yet, under the panel's reasoning, many AI innovations are potentially unpatentable-even if they are adequately described and nonobvious-because the panel essentially equated any machine learning with an unpatentable "algorithm" and the remaining additional elements as "generic computer components," without adequate explanation. Dec. 24. Examiners and panels should not evaluate claims at such a high level of generality.”
Further in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), the claimed invention was a method of training a machine learning model on a series of tasks. The Appeals Review Panel (ARP) overall credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements that were disclosed in the patent application specification. Specifically, the ARP upheld the Step 2A Prong One finding that the claims recited an abstract idea (i.e., mathematical concept). In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were
The claim itself does not need to explicitly recite the improvement described in the specification (e.g., “thereby increasing the bandwidth of the channel”). See, e.g., Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), in which the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation.
The second paragraph of MPEP § 2106.05(a), subsection I, is revised to add new examples xiii and xiv to the list of examples that may show an improvement in computer functionality:
xiii. An improved way of training a machine learning model that protected the model’s knowledge about previous tasks while allowing it to effectively learn new tasks; Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential); and
xiv. Improvements to computer component or system performance based upon adjustments to parameters of a machine learning model associated with tasks or workstreams; Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential).
Step 2B: DOES THE CLAIM RECITE ADDITIONAL ELEMENTS THAT AMOUNT TO SIGNIFICANTLY MORE THAN THE JUDICIAL EXCEPTION?
No. Claims amount to using joint probability distribution from a previous sample set to produce new samples such as through LPC mathematical operations, while a neural network is claimed, it is extra solution activity with the underlying principles of altered original signals unchanged.
• Collecting and comparing known information (Classen)
• Comparing data to determine a risk level (Perkin‐Elmer)†
• Comparing new and stored information and using rules to identify options (Smartgene)†
• Data recognition and storage (Content Extraction)
Assistance for Applicant in amending to overcome 101:
Limitations that the courts have found to qualify as “significantly more” when recited in a claim with a judicial exception include:
i. Improvements to the functioning of a computer, e.g., a modification of conventional Internet hyperlink protocol to dynamically produce a dual-source hybrid webpage, as discussed in DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258-59, 113 USPQ2d 1097, 1106-07 (Fed. Cir. 2014) (see MPEP § 2106.05(a));
ii. Improvements to any other technology or technical field, e.g., a modification of conventional rubber-molding processes to utilize a thermocouple inside the mold to constantly monitor the temperature and thus reduce under- and over-curing problems common in the art, as discussed in Diamond v. Diehr, 450 U.S. 175, 191-92, 209 USPQ 1, 10 (1981) (see MPEP § 2106.05(a));
iii. Applying the judicial exception with, or by use of, a particular machine, e.g., a Fourdrinier machine (which is understood in the art to have a specific structure comprising a headbox, a paper-making wire, and a series of rolls) that is arranged in a particular way to optimize the speed of the machine while maintaining quality of the formed paper web, as discussed in Eibel Process Co. v. Minn. & Ont. Paper Co., 261 U.S. 45, 64-65 (1923) (see MPEP § 2106.05(b));
iv. Effecting a transformation or reduction of a particular article to a different state or thing, e.g., a process that transforms raw, uncured synthetic rubber into precision-molded synthetic rubber products, as discussed in Diehr, 450 U.S. at 184, 209 USPQ at 21 (see MPEP § 2106.05(c));
v. Adding a specific limitation other than what is well-understood, routine, conventional activity in the field, or adding unconventional steps that confine the claim to a particular useful application, e.g., a non-conventional and non-generic arrangement of various computer components for filtering Internet content, as discussed in BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350-51, 119 USPQ2d 1236, 1243 (Fed. Cir. 2016) (see MPEP § 2106.05(d)); or
vi. Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment, e.g., an immunization step that integrates an abstract idea of data comparison into a specific process of immunizing that lowers the risk that immunized patients will later develop chronic immune-mediated diseases, as discussed in Classen Immunotherapies Inc. v. Biogen IDEC, 659 F.3d 1057, 1066-68, 100 USPQ2d 1492, 1499-1502 (Fed. Cir. 2011) (see MPEP § 2106.05(e)).
To help in amending the claims and for analysis purposes, example claims 3 and 4 are listed below from the courts, however such example amendment potentials are not limited to the provided examples and alternative amendments are possible using i-vi from the courts. The example below show differences between eligible claims (court claim 4) and ineligible claims (court claim 3), which thus illustrates significantly more which is tied to hardware that is not generally recited in the art. In this case general changing of font size in claim 3 versus a significant step of conditionally changing font size tied to hardware in claim 4.
See below examples based on MPEP and not on the current claim set, to help amend to overcome 101 rejections:
Regarding independent claim examples:
For instance in the example claims, for example claims 3 and 4 below:
Ineligible
3. A computer‐implemented method of resizing textual information within a window displayed in a graphical user interface, the method comprising:
(not significant) generating first data for describing the area of a first graphical element;
(not significant) generating second data for describing the area of a second graphical element containing textual information;
(not significant) calculating, by the computer, a scaling factor for the textual information which is proportional to the difference between the first data and second data.
The claim recites that the step of calculating a scaling factor is performed by “the computer” (referencing the computer recited in the preamble). Such a limitation gives “life, meaning and vitality” to the preamble and, therefore, the preamble is construed to further limit the claim. (See MPEP 2111.02.)
However, the mere recitation of “computer‐implemented” is akin to adding the words “apply it” in conjunction with the abstract idea. Such a limitation is not enough to qualify as significantly more. With regards to the graphical user interface limitation, the courts have found that simply limiting the use of the abstract idea to a particular technological environment is not significantly more. (See, e.g., Flook.)
Whereas in similar claim 4:
Eligible
4. A computer‐implemented method for dynamically relocating textual information within an underlying window displayed in a graphical user interface, the method comprising:
displaying a first window containing textual information in a first format within a graphical user interface on a computer screen;
displaying a second window within the graphical user interface;
constantly monitoring the boundaries of the first window and the second window to detect an overlap condition where the second window overlaps the first window such that the textual information in the first window is obscured from a user’s view;
determining the textual information would not be completely viewable if relocated to an unobstructed portion of the first window;
calculating a first measure of the area of the first window and a second measure of the area of the unobstructed portion of the first window;
calculating a scaling factor which is proportional to the difference between the first measure and the second measure;
scaling the textual information based upon the scaling factor;
(significant step) automatically relocating the scaled textual information, by a processor, to the unobscured portion of the first window in a second format during an overlap condition so that the entire scaled textual information is viewable on the computer screen by the user;
(significant step) automatically returning the relocated scaled textual information, by the processor, to the first format within the first window when the overlap condition no longer exists.
These limitations are not merely attempting to limit the mathematical algorithm to a particular technological environment. Instead, these claim limitations recite a specific application of the mathematical algorithm that improves the functioning of the basic display function of the computer itself. As discussed above, the scaling and relocating the textual information in overlapping windows improves the ability of the computer to display information and interact with the user.
The dependent claims are rejected as follows, for the same reasoning as being directed towards patent ineligible subject matter under 35 U.S.C. 101, and not adding eligible subject matter to the respective parent claim.
Claims 3, 4, 7-19, 24 and 27 are also directed to more specified concepts of using joint probability distribution from a previous sample set to produce new samples such as through LPC mathematical operations, for instance reconstruction per se and further LPC operations of which a person can do by hand. While complex, for instance in claims 8-18, mathematical or theoretical concepts are not precluded under BRI. In other words, a person can possibly perform the steps by hand or in a mathematical process independent of any hardware or software per se e.g. convolution, PCM, LPC, signal analysis transforms, etc.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
NOTE: The claims are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The claim limitations, specifically claims 1, 29, and 31 in question recite:
“a neural network…”.
“a sample generator…”
In the scope of software-hardware, such elements preceding “******” provide structure that aligns with the courts and is analogous to BRI examples such as a “digital detector” in the scope of computing per se, and a “knife blade” unit for cutting, in the scope of non-computing technology. Specifically considering the following:
E. “Detector”
1. Personalized Media Commc’ns, LLC, v. ITC, 161 F.3d 696 (Fed. Cir. 1998)
United States Patent 5,335,277 (“the ’277 patent”)
The claimed subject matter relates to an integrated system for communicating programming, e.g. electronically transmitted entertain, instruct or inform, including television, radio, broadcast print, etc. The relevant claim language of independent claim 44 of the ’277 patent is as follows:
“. . .a digital detector operatively connected to a mass medium receiver for detecting digital information in a mass medium transmission and transferring some of said detected information to a processor; . . .”
Issue: Does the claim limitation “digital detector” invoke 35 U.S.C. § 112, sixth paragraph?
Analysis: The claim limitation does not use “means for” language to invoke 35 U.S.C. § 112, sixth paragraph. To one of ordinary skill in the relevant art, the term “detector” connotes or describes in general a structure. The claim term “digital detector” is subsequently modified by the functional language “for detecting.” The fact that the term “detector” does not suggest a precise physical structure would not result in 35 U.S.C. § 112, sixth paragraph being invoked. The claim term “detector” is understood in the relevant prior art and defined in dictionaries as having a well known meaning in the electrical arts connotative of structure. Further, just because the claim term “detector” is a name for structure drawn from the function it performs, should not result in treatment under 35 U.S.C. 112, sixth paragraph. Therefore the term “detector” is structural and not a nonce word or a verbal construct that is not recognized as the name of structure and simply a substitute for the term “means for.” Accordingly, the presence of a structural term combined with the absence of any “means for” language indicates that 35 U.S.C. § 112, sixth paragraph is not invoked.
Conclusion: 35 U.S.C. § 112, sixth paragraph is not invoked.
The above analysis is in accordance with MPEP 2181 I 8th Ed. Rev. 6., Sept 2007 Pages 2100-236, and further in view of the analytical framework of the 2011 Supplementary Guidelines to determine whether a limitation invokes 35 U.S.C. § 112, sixth paragraph.
And additionally in view of the courts regarding “means” per se, the example of an Ink jet means for ink delivery modified by “ink jet” which is sufficient structure for achieving specified functions. If such elements e.g. unit, module, sensor, were recited on its own in the current claims, such interpretation would not be applicable, and instead a generic placeholder would be present, such as the sole mention of “device” or “apparatus” on its own would in fact invoke 112(f). In the case above, this is not reasonable for the field of software/hardware.
Such claims are believed to not exhibit:
1) a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function, and
2) “means” or “step”, and 2) usage of the word “means” or “step”.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3, 4, 7, 19, 24, 27, 29, and 31 rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 20220044694 A1 Klejsa; Janusz et al. (hereinafter Klejsa).
Re claim 1, Klejsa teaches
1. (Original) A device comprising: (0029)
a neural network configured to process one or more neural network inputs to generate a joint probability distribution, the one or more neural network inputs including at least first previous sample data and second previous sample data associated with at least one previous data sample of a sequence of data samples; and (SampleRNN e.g. autoregressive model as part of the GAN for reconstructing new audio samples based on previous audio samples using joint probability distribution 0074-0075)
a sample generator configured to generate first sample data and second sample data based on the joint probability distribution, the first sample data and the second sample data associated with at least one data sample of the sequence of data samples. (The samples themselves are created via at least SampleRNN e.g. autoregressive model as part of the GAN for reconstructing new audio samples based on previous audio samples using joint probability distribution 0074-0075)
Re claim 29, this claim has been rejected for teaching a broader, or narrower claim based on general inclusion of hardware alone (e.g. processor, memory, instructions), representation of claim 1 omitting/including hardware for instance, otherwise amounting to a virtually identical scope
Re claim 31, this claim has been rejected for teaching a broader, or narrower claim based on general inclusion of hardware alone (e.g. processor, memory, instructions), representation of claim 1 omitting/including hardware for instance, otherwise amounting to a virtually identical scope
For instance, see 0029 e.g. hardware with memory.
Re claim 3, Klejsa teaches
3. (Currently Amended) The device of claim [[2]] 1, wherein the sample generator comprises at least one linear prediction (LP) module configured to:
generate first reconstructed audio data based on first linear predictive coding (LPC) coefficients and the first audio residual sample data; and (as in 0083-0094 LPC with coefficients for SampleRNN e.g. autoregressive model as part of the GAN for reconstructing new audio samples based on previous audio samples using joint probability distribution 0074-0075)
generate second reconstructed audio data based on second LPC coefficients and the second audio residual sample data, wherein at least one reconstructed audio sample of an audio frame of a reconstructed audio signal is based on the first reconstructed audio data, the second reconstructed audio data, or both. (as in 0083-0094 LPC with coefficients for SampleRNN e.g. autoregressive model as part of the GAN for reconstructing new audio samples based on previous audio samples using joint probability distribution 0074-0075)
Re claim 4, Klejsa teaches
4. (Original) The device of claim 3, wherein the second LPC coefficients are the same as the first LPC coefficients. (nothing precluding a null or 1 coefficient value across the board for nth, as in 0083-0094 LPC with coefficients for SampleRNN e.g. autoregressive model as part of the GAN for reconstructing new audio samples based on previous audio samples using joint probability distribution 0074-0075)
Re claim 7, Klejsa teaches
7. (Currently Amended) The device of claim [[6]] 3, wherein the one or more neural network inputs include LP residual data, LP prediction data, or both, associated with [[the]] a first previous reconstructed audio sample of the reconstructed audio signal, [[the]] a second previously reconstructed audio sample of the reconstructed audio signal, one or more additional previous reconstructed audio samples, or a combination thereof. (residual LPC as in 0083-0094 LPC with coefficients for SampleRNN e.g. autoregressive model as part of the GAN for reconstructing new audio samples based on previous audio samples using joint probability distribution 0074-0075)
Re claim 19, Klejsa teaches
19. (Currently Amended) The device of claim 3, wherein the at least one LP module is configured to generate the first reconstructed audio data further based on long-term linear prediction (LTP) data associated with the first reconstructed audio data, LP data associated with the first reconstructed audio data, previous LP residual data associated with the first previous [[audio]] sample data, LP residual data associated with the first reconstructed audio data, or combination thereof. (0083-0094 LPC with coefficients for SampleRNN e.g. autoregressive model as part of the GAN for reconstructing new audio samples based on previous audio samples using joint probability distribution 0074-0075)
Re claim 24, Klejsa teaches
24. (Original) The device of claim 3, further comprising: a modem configured to receive encoded audio data from a second device; and a decoder configured to decode the encoded audio data to generate the first LPC coefficients and the second LPC coefficients. (0083-0094 LPC with coefficients for SampleRNN e.g. autoregressive model as part of the GAN for reconstructing new audio samples based on previous audio samples using joint probability distribution 0074-0075)
Re claim 27, Klejsa teaches
27. (Original) The device of claim 1, wherein the neural network includes an autoregressive (AR) generative neural network. (SampleRNN e.g. autoregressive model as part of the GAN for reconstructing new audio samples based on previous audio samples using joint probability distribution 0074-0075)
Allowable Subject Matter
Claims 8-18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. After searching through patent and non-patent literature, there was no evidence that there exists a limitation in direct relation or an obvious variant to such limitations as a whole as precisely limited. When searching for a secondary prior art for the limitation as recited in the above claims, the most relevant topics pertained to material from the same Inventor and Assignee but did not teach or suggest the aforementioned complex limitations as a whole as precisely limited.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20220078561 A1 LAI; CHAO-JUNG et al.
Neural network sub-band analysis
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